Papers with Code 아카이브

Electromyography (EMG)

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Giguelingueling/MyoArmbandDataset

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Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

https://github.com/Mak-Sim/Troparion

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Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato

https://github.com/UlysseCoteAllard/LongTermEMG

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A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition

https://github.com/Westlake-AI/OpenBioSeq

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Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining

https://github.com/a-moin/flexemg

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An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier

https://github.com/bohu615/corticomuscular-eigen-encoder

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Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios

https://github.com/coreylammie/TBCAS-Towards-Healthcare-and-Biomedical-Applications

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Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

https://github.com/cwdao/paper_code_robustness_enhanced_myoelectric_control

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Robustness-enhanced Myoelectric Control with GAN-based Open-set Recognition

https://github.com/davidruegamer/BoostingSignalSynchro

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Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals

https://github.com/deanzadok/finemotions

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Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation

https://github.com/dgaddy/silent_speech

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Digital Voicing of Silent Speech

https://github.com/dlcjfgmlnasa/SynthSleepNet

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Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework

https://github.com/dreamento/dreamento

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Dreamento: an open-source dream engineering toolbox for sleep EEG wearables

https://github.com/jehanyang/emgbench

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EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography

https://github.com/josephsdavid/semg_repro

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sEMG Gesture Recognition with a Simple Model of Attention

https://github.com/ketyi/2SRNN

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Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks

https://github.com/killingbear999/eeg-emg-lstm

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Upper Limb Movement Recognition utilising EEG and EMG Signals for Rehabilitative Robotics

https://github.com/klotz-t/MUDecompositionTests

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Revisiting convolutive blind source separation for identifying spiking motor neuron activity: From theory to practice

https://github.com/lee-jhwn/is25-emg-ema

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Articulatory Feature Prediction from Surface EMG during Speech Production

https://github.com/nik-sm/com-hom-emg

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Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder

https://github.com/nikhil-garg/EMG_exp

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Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition

https://github.com/olivesgatech/DARai

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Hierarchical and Multimodal Data for Daily Activity Understanding

https://github.com/pingala-institute/Bayes-medicaldataimputation

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Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation

https://github.com/sharif1093/dextron

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End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning

https://github.com/wanglichenxj/EV-Action-Electromyography-Vision-Multi-Modal-Action-Dataset

EV-Action: Electromyography-Vision Multi-Modal Action Dataset

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.